Nonvolatile Memristive Materials and Physical Modeling for In‐Memory and In‐Sensor Computing

Separate memory and processing units are utilized in conventional von Neumann computational architectures. However, regarding the energy and the time, it is costly to shuffle data between the memory and the processing entity, and for data‐intensive applications associated with artificial intelligence, the demand is ever increasing. A paradigm shift in traditional architectures is required, and in‐memory computing is one of the non‐von‐Neumann computing strategies. By harnessing physical signatures of the memory, computing workloads are administered in the same memory element. For in‐memory computing, a wide range of memristive material (MM) systems have been examined. Moreover, developing computing schemes that perform in the same sensory network and that minimize the data shuffle between the processing unit and the sensing element is a requirement, to process large volumes of data efficiently and decrease the energy consumption. In this review, an overview of the switching character and system signature harnessed in three archetypal MM systems is rendered, along with an integrated application survey for developing in‐sensor and in‐memory computing, viz., brain‐inspired or analogue computing, physical unclonable functions, and random number generators. The recent progress in theoretical studies that reveal the structural origin of the fast‐switching ability of the MM system is further summarized

Nonvolatile Memristive Materials and Physical Modeling for In‐Memory and In‐Sensor Computing

Publication status: PublishedSeparate memory and processing units are utilized in conventional von Neumann computational architectures. However, regarding the energy and the time, it is costly to shuffle data between the memory and the processing entity, and for data‐intensive applications associated with artificial intelligence, the demand is ever increasing. A paradigm shift in traditional architectures is required, and in‐memory computing is one of the non‐von‐Neumann computing strategies. By harnessing physical signatures of the memory, computing workloads are administered in the same memory element. For in‐memory computing, a wide range of memristive material (MM) systems have been examined. Moreover, developing computing schemes that perform in the same sensory network and that minimize the data shuffle between the processing unit and the sensing element is a requirement, to process large volumes of data efficiently and decrease the energy consumption. In this review, an overview of the switching character and system signature harnessed in three archetypal MM systems is rendered, along with an integrated application survey for developing in‐sensor and in‐memory computing, viz., brain‐inspired or analogue computing, physical unclonable functions, and random number generators. The recent progress in theoretical studies that reveal the structural origin of the fast‐switching ability of the MM system is further summarized.</jats:p

High‐Performance Graphene‐Dielectric Interface by UV‐Assisted Atomic Layer Deposition for Graphene Field Effect Transistor

Abstract The deposition of high‐quality dielectric films on graphene surfaces is crucial in fabricating high‐performance graphene‐based electronics. In this study, the first application of UV‐assisted atomic layer deposition (UV‐ALD) to graphene surfaces and the fabrication of graphene field‐effect transistors (GFETs) with UV‐ALD Al2O3 dielectric thin films is demonstrated. Optimal UV irradiation (5 s per cycle) during the ALD process results in denser and smoother Al2O3 dielectric films deposited on the graphene surface with the intimate graphene‐dielectric interface, while excessive UV irradiation in turn prohibits the film nucleation. As a result, the GFETs with a high‐quality dielectric layer deposited by UV‐ALD show improved performance with a Dirac voltage close to 0 V and hole mobility of 1221 cm2 V−1 s−1, i.e., > 200% increase compared to those with thermal ALD. This study demonstrates that UV‐ALD is an effective and simple option to realize a high‐quality interface between 2D materials and ultra‐thin dielectric films

Toward Single-Cell Multiple-Strategy Processing Shift Register Powered by Phase-Change Memory Materials

Modern innovations are built on the foundation of computers. Compared to von Neumann architectures having separate storage and processing units, in‐memory operation utilizes the same primary structure for data storage and register operations, therefore promising to decrease the energy cost of computing in data centers significantly. While various studies centered on exploring novel device architectures, designing suitable material platforms is extremely challenging. Herein, all four material (M) states of a phase‐change material (PCM) in data storage and register operations are utilized and a combined M state‐based model framework for developing in‐memory operation is demonstrated, along with nonvolatile, reprogrammable single‐cell shift register operations. A previously unachieved multiple‐level‐per‐volt different‐initial‐state multilevel set process with further computing in the M state‐based platform is realized. The simplest case of a programmable shift register configuration is demonstrated with a serial‐in–serial‐out processing strategy, as well as more complex reprogrammable processing schemes using the M state‐type platform, showing previously unreported nonvolatile shift register types with multiple processing approaches. This paves the way for development of next‐generation low‐power‐electronic systems using two‐terminal‐based semiconductor materials

Ultrafast Near-Ideal Phase-Change Memristive Physical Unclonable Functions Driven by Amorphous State Variations.

Funder: SUTD‐MIT International Design CenterThere is an ever-increasing demand for next-generation devices that do not require passwords and are impervious to cloning. For traditional hardware security solutions in edge computing devices, inherent limitations are addressed by physical unclonable functions (PUF). However, realizing efficient roots of trust for resource constrained hardware remains extremely challenging, despite excellent demonstrations with conventional silicon circuits and archetypal oxide memristor-based crossbars. An attractive, down-scalable approach to design efficient cryptographic hardware is to harness memristive materials with a large-degree-of-randomness in materials state variations, but this strategy is still not well understood. Here, the utilization of high-degree-of-randomness amorphous (A) state variations associated with different operating conditions via thermal fluctuation effects is demonstrated, as well as an integrated framework for in memory computing and next generation security primitives, viz., APUF, for achieving secure key generation and device authentication. Near ideal uniformity and uniqueness without additional initial writing overheads in weak memristive A-PUF is achieved. In-memory computing empowers a strong exclusive OR (XOR-) and-repeat A PUF construction to avoid machine learning attacks, while rapid crystallization processes enable large-sized-key reconfigurability. These findings pave the way for achieving a broadly applicable security primitive for enhancing antipiracy of integrated systems and product authentication in supply chains

Theoretical Study on Enhancement of Sensing Capability of Plasmonic Dimer Au Nanoparticles with Amphiphilic Polymer Brushes

Au nanoparticle (Au-NP) sensors need a high surface plasmon resonance intensity and a low steric effect for efficient labeling in sensors. Since dimers meet these requirements, we have theoretically studied the self-assembly of monomer and dimer Au-NPs by considering influential factors such as Au-NP size, polymer thickness, and gap distance between dimer Au-NPs. In order to control the monomerization and dimerization of spherical Au-NPs and their sizes via self-assembly, two polymers (hydrophilic PEG and hydrophobic PMMA) were grafted on the Au-NPs as amphiphilic brushes. Computational methods of dissipative particle dynamics and discrete dipole approximation were employed for virtual self-assembly and theoretical analyses of plasmons related to sensing properties, respectively. We found that the bigger Au-NPs were obtained when the amounts of each polymer were roughly identical and the gap distance between Au-NPs in the dimer was shorter when the amount of PMMA was reduced within the condition of dimerization. This theoretical study revealed an optimal near-contact distance for Au-NPs@PMMA/PEG, where the electron tunneling effect was minimized, and reported unseen roles of polymers and plasmons, which consequently allowed achieving a highly efficient Au-NP dimer sensor.clos